Course Information

Class Description

CSE 537 provides comprehensive introduction to the problems of artificial intelligence (AI) and techniques for attacking them. Include traditional topics of AI as well as advanced topics of machine learning: problem representation, problem-solving methods, search, pattern recognition, planning and learning, and AI programming. The course covers both theoretical methods and practical implementations.


Assistant Professor Sael Lee
Office: Academic Bldg. B422
Email: sael at sunykorea dot ac dot kr
Phone: +82 (32) 626-1215

Meeting Time

[lecture] Mon/Wed 14:30~15:50 Academic Bldg. B204

Office Hours

Office Hours: Wed. 13:00-14:30 & Wed. 17:30-18:00 (or send emails for appointments) at B422


MAT 371 or CSE 541; Graduate standing; Working knowledge of programming.


Artificial Intelligence: A Modern Approach 3rd Edition by Stuart Russell and Peter Norvig (book website)

Pattern Recognition and Machine Learning by Christopher M. Bishop


Assignments: 15%; Project: 35%; Midterm: 25%; Finals: 25%


There will be assignments every two or so weeks. (4~5 in total)


There will be a middle evaluation composed of proposal (5%) and final evaluation composed of a full report (25%) and presentation (5%).


Project proposal: 1 page.
Guidlines on writing your proposal and report: cse549 project doc and cse549 project guideline;

2015-11-12: Assignment 3 out. (Due on 11/25)

course materials

(will be updated next week)
# date content aima reading assignments slides
1 8/31 introduction & intelligent agents 1 ch1 ch2
9/2 problem solving & uninformed searching 3(1-4) ch3a
2 9/7 informed (heuristic) searching 3(5-6) ch3b
9/9 local search 4(1-2) ch04a sa ga
3 9/14 nondeterministic & partially observable problems 4(3-5) ch04b
9/16 minimax/alpha-beta pruning 5(1~3) ch05
4 9/21 constraint satisfaction problems 6 ch06a
9/23 constraint satisfaction problems 6 hw1 (due 10/12; submit in class) ch06b
5 9/28 no class: national holiday
9/30 no class: correction day
6 10/5 logical agents 7.1-3 ch07a
10/7 logical agents cont. 7.4-6 ch07b
7 10/12 classical planning 10(1-2) ch10a
10/14 classical planning cont. 10(3-4) proposal deadline;
8 10/19 midterm exam: in class (tentative)
10/21 knowledge representation 12 ch12
9 10/26 quantifying uncertainty 13 ch13
10/28 probabilistic reasoning - baysian networks 14(1-4) ch14a;
10 11/2 probabilistic reasoning - baysian networks cont. 14(5) ch14b
11/4 probabilistic reasoning - baysian networks cont. 14(5) ch14c ch14d
11 11/9 probabilistic reasoning over time 15 ch15a
11/11 probabilistic reasoning over time cont 15 HW3(Due Nov 25th 2:30pm) ch15b
12 11/16 learning from examples 18.1-3 ch18a
11/18 learning from examples cont. 18.4-5 ch18b
13 11/23 learning from examples cont. 18.7-8 ch18c
11/25 learning from examples cont. 18.9 ch18d
14 11/30 learning from examples cont. 19 ch18e
12/2 learning probabilistic models 20 HW4 (due 9th in class) ch20a
15 12/7 project presentation Vasundhara Dehiya & Zhiqian Liu; Yongjin Park; Qingjiang Yin; presentation
12/9 project presentation Yulingfei Li & Anvika Kumar; Harley Jackson; Maruf Ahmad; Homin Yoon; project report deadline
fw 12/14 final exam: 15:30-17:30am(fixed)

** course slides are modified and recompiled version of slides provided in aima webpage.

course policy

attendance policy

everyone is strongly urged to attend class regularly and actively participate. you will be responsible for learning all the materials covered in class. notes and supplementary handouts will cover most of the material; however, in-class participation through engaging in discussions and asking questions should be valued learning activity.

assignments grading policy

assignment will be handed out in class and are due in class of the due date. total points of each assignment will be different depending on the difficulty of the problems. however, the maximum total point of an assignment will be less than or equal to two times the minimum total point of an assignment. expect to see difficult problems towards the end of semester.

you have budget of 5 days that you may submit your assignments late in total throughout the semester. spend them as you will. there will be 10% late penalty for each day late exceeding the 5 day grace. there are no extensions given to individuals unless it is an extreme case of a proven emergency (this does not include family emergencies).

academic misconduct policy

there is no excuse in cheating. cheating will be considered as an academic misconduct and handled according to the stony brook regulations. if cheating has occurred during exam or is evident in submitted assignments, your will get a grade of f. discussion of assignments is acceptable, however, returned assignments must show originality. this means near duplicate assignments with your peers or duplications of materials found on the web will be considered cheating. all involved personals in cheating will be penalized.

university policy

americans with disabilities act

if you have a physical, psychological, medical or learning disability that may impact your course work, please contact disability support services, ecc(educational communications center) building, room 128, (631)632-6748. they will determine with you what accommodations, if any, are necessary and appropriate. all information and documentation is confidential.disability support services.

academic integrity

each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. representing another person's work as your own is always wrong. faculty is required to report any suspected instances of academic dishonesty to the academic judiciary. faculty in the health sciences center (school of health technology & management, nursing, social welfare, dental medicine) and school of medicine are required to follow their school-specific procedures. for more comprehensive information on academic integrity, including categories of academic dishonesty please refer to the academic judiciary website at academic judiciary

critical incident management

stony brook university expects students to respect the rights, privileges, and property of other people. faculty are required to report to the office of university community standards any disruptive behavior that interrupts their ability to teach, compromises the safety of the learning environment, or inhibits students' ability to learn. faculty in the hsc schools and the school of medicine are required to follow their school-specific procedures. further information about most academic matters can be found in the undergraduate bulletin, the undergraduate class schedule, and the faculty-employee handbook.